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Due to the recent popularity of technologies like machine learning, artificial intelligence, and deep learning, many people are now trying to change their tracks into these fields due to an increased demand of trained professionals in the same but are confused about the application of such things like pattern recognition, then this article might just be for you because in it we will be discussing topics like what is pattern recognition and how it is taken into use in this technical area.

Introduction and overview: pattern recognition is somewhat synonymous to the term machine learning and experienced people in this area will know why because in pattern recognition, the machine is trying to find similar patterns based on the similarity of the sets provided by the training data. In simpler words, it means that it will point out the regularities in the data and hence make an appropriate conclusion out of the same. the main data that is training these sets of patterns is known as the training data and is mostly a set of information which is based on multiple individual calculations and other conclusions. sometimes cases are there when there is no training data and the work has to be started with a new end, in this case, we can use several present algorithms that can help us in reaching to an appropriate output. if we consider a Venn diagram of machine learning, artificial intelligence, deep learning and pattern recognition, we will be able to see that they all will be overlapping each other and hence it can be concluded that they come under the same thing. pattern recognition can be seen as a work given to the machine under the machine learning compartment to find a specific pattern and then making a conclusion out of it will be the work of the machine itself. the work of the same is to either make a conclusion or generalize the data as much as possible. most of the times, pattern recognition is used when the machine is stuck between an argument to choose between two of the most appropriate outcomes it thinks are possible. the problem here is that the algorithm will be thinking that both the cases are correct but it is the work of pattern recognition to choose between the two and provide the same output to the machine as well. this case of learning that was just explained is also known as semi-supervised learning because there is no total supervision by the algorithms and also the fact that there was some help taken by the pattern recognition and the training dataset.

Pattern recognition algorithms: the algorithms that can be used for pattern recognition can be divided into two parts that are probabilistic and nonprobabilistic. the probabilistic algorithms can be defined as the ones which use some kind of statistics in them. this can be explained as this kind of algorithms use a specific way that incorporates probability in its decision to find out the best of them all and give the best output instead of just finding out the best on the basis of certain parameters. the reasons why probabilistic algorithms are preferred over the no one's is because they have a certain advantage over the other and hence they are more preferred. some of the advantages are given as follows :

the output provided by this kind of algorithms are mostly accurate because they are based on the theory of probability and hence it makes them more accurate than the others. certain kind of values known as the confidence values are generated by the probabilistic algorithms that are based on mathematics whereas the other algorithms are just based out the best that is thought by the system.

Another advantage that comes in handy is because they are being backed by the mathematics behind them, they can be used in machine learning processes that are huge because when the output of one process is dependant on the other, the chances of error in the output must be reduced to a bare minimum and hence they should be preferred over other algorithms. the other reasons being that if an error is once made in huge and large processes, it kinds of ruins the whole system because the error only acts as a feedback in the system.

Frequentist approach: this is the kind of approach in the area of pattern recognition where the given conditions on the system are unknown but the main result or we can say the objective of the same is provided hence it can be easy to find the appropriate result. The reason this is also sometimes preferred is that the parameters or the conclusion are estimated from the information collected by the system and hence is important. later it went to a modified version where the distinction between calculation and observation was done to calculate the result and later came to be known as the Bayesian statistics where the mixture of probability and an expert observation was made to facilitate the results.

Advantages of pattern recognition: with the increase in the use of machine learning and pattern recognition in various fields, there are a variety of advantages to the same given as as follows:

Computer aided diagnosis: this is the term which is mostly denoted by CAD which means that a suitable conclusion about the patient's condition is reached with the observation and the findings of a doctor with the help of a computer which uses all the set of input data as well as the algorithms that are fed into the same to using the help of a certified doctor.

Other uses that are not related to the medical industry include some like license plate recognition where if any vehicle is stolen or has been used for some unreasonable purposes then the same can be easily tracked with the help of these sensing algorithms with the help of image processing. Pattern recognition can be combined with the image processing to perform many other tasks related to the same.

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